Synapse coupling can benefit signal exchange between neurons and information encoding for neurons, and the collective behaviors such as synchronization and pattern selection in neuronal network are often discussed under chemical or electric synapse coupling. Electromagnetic induction is considered at molecular level when ion currents flow across the membrane and the ion concentration is fluctuated. Magnetic flux describes the effect of time-varying electromagnetic field, and memristor bridges the membrane potential and magnetic flux according to the dimensionalization requirement. Indeed, field coupling can contribute to the signal exchange between neurons by triggering superposition of electric field when synapse coupling is not available. A chain network is designed to investigate the modulation of field coupling on the collective behaviors in neuronal network connected by electric synapse between adjacent neurons. In the chain network, the contribution of field coupling from each neuron is described by introducing appropriate weight dependent on the position distance between two neurons. Statistical factor of synchronization is calculated by changing the external stimulus and weight of field coupling. It is found that the synchronization degree is dependent on the coupling intensity and weight, the synchronization, pattern selection of network connected with gap junction can be modulated by field coupling.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5778049 | PMC |
http://dx.doi.org/10.1038/s41598-018-19858-1 | DOI Listing |
Front Public Health
January 2025
Center for Food Animal Health, The Ohio State University, Columbus, OH, United States.
Introduction: Enteric pathogens are a leading causes of diarrheal deaths in low-and middle-income countries. The Exposure Assessment of Infections in Rural Ethiopia (EXCAM) project, aims to identify potential sources of bacteria in the genus and, more generally, fecal contamination of infants during the first 1.5 years of life using as indicator.
View Article and Find Full Text PDFFront Cell Neurosci
January 2025
The Research Center for Brain Function and Medical Engineering, Asahikawa Medical University, Asahikawa, Japan.
The evolution of brain-expressed genes is notably slower than that of genes expressed in other tissues, a phenomenon likely due to high-level functional constraints. One such constraint might be the integration of information by neuron assemblies, enhancing environmental adaptability. This study explores the physiological mechanisms of information integration in neurons through three types of synchronization: chemical, electromagnetic, and quantum.
View Article and Find Full Text PDFNat Mater
January 2025
School of Physics, Zhejiang University, Hangzhou, China.
In ordered magnets, the elementary excitations are spin waves (magnons), which obey Bose-Einstein statistics. Similarly to Cooper pairs in superconductors, magnons can be paired into bound states under attractive interactions. The Zeeman coupling to a magnetic field is able to tune the particle density through a quantum critical point, beyond which a 'hidden order' is predicted to exist.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Medical Life Sciences, College of Medicine, The Catholic University of Korea, Seoul, 06591, Korea.
Human cerebral organoids serve as a quintessential model for deciphering the complexities of brain development in a three-dimensional milieu. However, imaging these organoids, particularly when they exceed several millimeters in size, has been curtailed by the technical impediments such as phototoxicity, slow imaging speeds, and inadequate resolution and imaging depth. Addressing these pivotal challenges, our study has pioneered a high-speed scanning microscope, synergistically coupled with advanced computational image processing.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Clinical Pharmacy, School of Pharmacy, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
Integrating machine learning (ML) models into healthcare systems is a rapidly evolving field with the potential to revolutionize care delivery. This study aimed to classify fertility rates and identify significant predictors using ML models among reproductive women in Ethiopia. This study utilized eight ML models in 5864 reproductive-age women using Ethiopian Demographic Health Survey (EDHS), 2019 data.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!